code: def is_traceable_wrapper_subclass(t):
      """
      Returns whether or not a tensor subclass that implements __torch_dispatch__
      is 'traceable' with torch.compile.
      In order for a tensor subclass to support TorchDispatchMode-style tracing in PT2,
      It must implement two magic methods: __tensor_flatten__ and __tensor_unflatten__.
      It is also expected to obey some restrictions around traceability and aliasing:
          * The subclass's __torch_dispatch__() implementation should desugar into pytorch
              dispatcher operations that can be traced into a graph.
          * The subclass should use return_and_correct_aliasing(). This is needed today to make
              sure that torch.compile does the right thing in a few cases around input mutation
              and output aliasing.
  
      Expected magic method signatures:
          attrs, ctx = t.__tensor_flatten__()
              attrs: list of attribute name strings for inner tensors
              ctx: dict containing any other subclass-specific metadata needed for unflattening
  
          t = MySubClass.__tensor_unflatten__(inner_tensors, ctx, outer_size, outer_stride)
              inner_tensors: dict mapping attribute name -> tensor for each inner tensor
              ctx: dict with subclass metadata in the form that __tensor_flatten__() produces
              outer_size: expected (possibly symbolic) size that the returned subclass
                  instance should have. Note that this arg is useful for certain subclasses
                  that require the shape info to be constructed. In most cases, this arg can be
                  safely ignored.
              outer_stride: expected (possibly symbolic) stride that the returned subclass
                  instance should have. Note that this arg is useful for certain subclasses
                  that require the stride info to be constructed. In most cases, this arg can be
                  safely ignored.
      """
      is_subclass = (
          isinstance(t, torch.Tensor) and type(t) != torch.Tensor
      )
      return (
          is_subclass
          and hasattr(t, "__tensor_flatten__")
          and hasattr(t, "__tensor_unflatten__")
      )
  
imports: import contextlib
  from typing import Optional, Union, List, Set, Dict, Any
  import warnings
  from dataclasses import dataclass
  import torch
  import torchgen
  from torch._C import _len_torch_dispatch_stack, _get_dispatch_stack_at, _pop_torch_dispatch_stack, _push_on_torch_dispatch_stack, DispatchKey
label_desc: Use image node_id 8 for calling a global function with example usage: is_traceable_wrapper_subclass(t) and returns: unknown